| With the fast development of national economy, total oil consumption is increasing. In1991, the national growth rate of oil consumption was greater than the crude production. Since1993, China has changed from the net exporter to the net importer, and the oil consumption gap is still increasing. In the white paper of 《Chinese Energey Policy》 issued by the State Council in2012, Chinese crude oil production is207.48million tons that the year-on-year growth is1.9%, but the import quantity is271.09million tons that year-on-year growth is7.3%. Moreover, the foreign-trade dependence has reaches to56.4%, which will continue to rise in the future. As an important kind of non-renewable natural resource, oil resource has great influence on the economic development, political stability, military security and diplomatic policy. Therefore, China has regarded the petroleum resource as strategic resource, which affects the sustainable development of society and economy. After the developments in50years, our country has formed the strategy pattern of "Two kinds of resources, two kinds of markets", on the one hand, it’s necessary to strengthen the communication and cooperation with international petroleum producer country, and make use of international resources to make up for our own consumption gap; on the other hand, our country should pay attention to the exploration, development and utilization of domestic production oilfields, the oilfields also need to keep stable or incremental production so as to improve the rate of utilization and self-sufficiency.However, the domestic production situation is severe. Referred to the statistics, most overland oilfield has entered into the declining stage. In this stage, the production is decreasing, the comprehensive moisture content is close or over than90%and the degree of recoverable reserves is relatively in high level. For this reason, the traditional large-scale oilfields have accessed to the ultra-high water cut stage and high recovery degree stage, and its economic efficiency isn’t very well. For these great oilfields, how to reflect the present exploration situation accurately and objectively through the quantitative indicators? How to realize the future trend prediction of development indexes? How to reflet and identify the exploration potential and prospect of targeted oilfield? How to make comprehensive evaluation on technology benefits and economic benefits? These questions are worth thinking and researching. The article chooses a typical trial zone in DaQing Oilfield as the objective of study, and makes the variation prediction and benefit evaluation of development indexes as research contents, which has good theoretical research value and strong practical application significance.As to the above questions mentioned, the innovations are reflected in the following aspects:Firstly, how to reflect the present exploration situation accurately and objectively through the quantitative indicators? A large number of statistics has been accumulated and recorded in the production process, but these statistics often are redundant, incomplete and complex, which are hard to be used directly. Therefore, how to identify these multifarious statistics through the effective methods for extracting useful information could achieve scientific and reasonable evaluation indexes system in the oilfield exploration evaluation process, and this will be the research key in the article. In the process of selecting evaluation indexes, the article integrates the soft computing and hard computing to create the SVM-FCM future selection model based on the correlation analysis. The evaluation results are divided into5kinds (very good, good, medium, poor and very poor), and then the article puts forward to related technical indicators that influences oilfield exploration benefit, such as production days, pump depth, dynamic fluid level, moisture content, oil production and production decline rate. Finally through the calculation of SVM sensitivity and the absolute value of correlation coefficient, the key indicator is selected and applied as production decline rate.Secondly, how to realize the future trend prediction of development indexes? BP neural network has the strong ablity of parallel storage, parallel computing, elf learning and nonlinear mapping, which is for complicated geological statistics. However, the BP neural network algorithm is essentially a local search algorithm, and the accuracy of prediction results largely depend on the accuracy of the sample statistics. If using the global search ability of genetic algorithm to optimize network weights and threshold value, it will well improve the convergence rate and prediction accuracy, which the relationships between BP neural network and genetic algorithm are complementary and helpful to gain the accuracy and applicability in the predicition model. Using genetic algorithm optimizes BP neural network in the procution prediction model. The imput factors are determined as comprehensive moisture content, production decline rate, flow coefficient and recovery, on the opposite, the average monthly production is regarded as the key output indicator. The following step is the normalization of original statistics, and the normalization can eliminate the disadvantage influences because of dimensional differences among various indexes. At last, through combining with BP neural network and genetic algorithm to gain the individual coding of each factor, the article has realized the prediction of oilfield production. The results show that the simulation prediction of average monthly production has good consistency with the original training data, which means that using genetic algorithm to optimize the BP neural network has higher prediction accuracy.Thirdly, how to reflet and identify the exploration potential and prospect of targeted oilfield? From the establishment of quantitative indicators system reflecting remaining oil saturation and remaining oil reserves abundance, the article builds the evaluation models of remaining oil potential and water-flooding development effectiveness. And then, the model applies variation coefficient method to get each index weight, and make scientific classification effectively on development potential in typical trial zone.Fourthly, how to make comprehensive evaluation on technology benefits and economic benefits? Most oilfields face the situation of multi-well, lower production, high water cut and exploration. If the evaluations are made from technology benefits or economic benefit unilaterally, it will take a part for the whole that is difficult to do quantitative evaluation. Therefore, how to make a qualitative and quantitative description on oilfiled benefit through elaborate and accurate methods? Oilfield development benefit is influenced by many kinds of factors, including market economy, geological condition, political planning and processing equipment. Therefore, the comprehensive evaluation based on fuzzy mathematics degree theory makes an integral analysis on oilfield development benefit via qualitative and quantitative methods.The major contents are introduced as following:The first chapter is preface. The chapter briefly introduces the background and significance of the topic research, and also summaries the research content, structure and main innovation points.The second chapter is the optimization selection of evaluation indexes. The article briefly introduces historical development and application value of attribute reduction, as shown, the commonly methods are included dissimilarity matrix, heuristic attribution, non-completeness system, distributed and parallel type algorithm, feature transformation and subset selection. Based on these theoretical foundations and actual production situations (original attribute set includes:production day, pump depth, dynamic fluid level, moisture content and oil production), this chapter combines the hard computing model with soft computing model in the process of constructing evaluation indexes system. The model builds5different SVM sensitivity analyzers and makes correlation analysis on SVM-FCM feature selection. After calculating the sensitivity value of every index in original attribute set to finish the initial attribution reduction, the absolute value of correlation coefficients for the variables will help to identify the optimal characteristics. The benefit evaluation indexes in our country have some limits in indicators normalization, methods science and results objectivity, and these limits are just the key of research.The third chapter is benefit evaluation of development technique. In the view of development technology benefit, this part comprehensive reflects exploration situation of typical trial zone in DaQing Oilfield from comprehensive moisture content, production status, cumulative production and incremental production, production declining rate and well spacing density five aspects. In addition, in the process of describing production declining rate, the variation coefficient among different influenced factors shows that the key factors are the changes of well spacing density, production pressure difference, flow coefficient and water coefficient. In the chapter, the well spacing density indexes separately introduce the concept of economic reasonable well spacing density and economic limit well pattern density. In different conditions such as different income levels, different investment periods, different oil prices and different tax levels, the density threshold values will provide feasible suggestions for making production decisions and planning selection.The forth chapter is production prediction based on the genetic algorithm to optimize BP neural network. In the actual operation process, the geological statistics of oilfield in the high water cut stage have the characteristics of complication, so in the model of production prediction, this chapter combines BP neural network with the genetic algorithm GA, and uses genetic algorithm to optimize weights and threshold value in BP, which will effectively reduce the prediction uncertainty and improve the statistics quality. In this way, the BP neural network can achieve stable state and better results accuracy than before, and finally gain the optimal prediction results. In the case study, considering the principles of availability, comprehensiveness, oscillatory, comparativeness and independence, the chapter selects comprehensive moisture content, production decline rate, flow coefficient and injection production ratio as input factors, and then the average monthly production as output factor. The consistency of training data and testing data shows that using the genetic algorithm to optimize BP neural network is equipped with high accuracy, therefore this method is more suitable for production prediction in high water cut stage.The fifth chapter is dynamic evaluations of economic benefits. From the view of economic benefits, the chapter respectively uses investment payback period, net present value, internal rate of return, return on investment ratio, profit and tax investment ratio to make economic benefit evaluation for typical test zone in DaQing Oilfield, which is the foundation of projects total investment estimation in2009. Furthermore, taking the particularity of production decision into consideration, this part transforms economic benefits into economic production and economic reserve, and then put forward the dynamic economic limit production and economic limit reserve, so these two indexes make economic evaluation more directly and visually. In the calculation model based on the time value of money, market price fluctuation and cost changeability, these indexes utilize the profit and loss balance of net present value to achieve economic limit value, which has great significance in increasing economic warning consciousness and making scientific and reasonable investment decisions.The sixth chapter is the potential evaluation of remaining petroleum. How to understand the distribution law of remaining petroleum and how to carry on the potential evaluation are the keys of comprehensive adjustment on well spacing density in the late exploration stage. The chapter introduces the evaluation thoughts:first of all, the water flooded conditions are described through oilfield relative permeability and saturation; at the same time, the economic reserves abundance of remaining petroleum is described through the economic spacing density and economic reserves of every single well; finally, the simultaneous methods are combined together to make a comprehensive evaluation of potential evaluation. The research results shows that BED Test Zone in DaQing Oilfield has entered into the high water cut stage, and its water flooding status reaches the "strong flooding" level. However, the recoverable reserves abundance is far higher than the the economic reserves abundance, which means that it has enough recoverable space.The seventh chapter is comprehensive evaluation of overall exploration efficiency. The former part respectively makes an evaluation from the technology effectiveness and economic effectiveness, but how to reflect the comprehensive effectiveness of the overall regional? This is the key research contents in this part. The chapter divides the evaluation indexes into technology indexes, management indexes and economic indexes, and then selects11specific evaluation indexes including adjustment condition of well network etc. Besides, the weights of indexes are determined through the coefficient of variation, and the related evaluation grades are constructed by the model of ridge membership function. Finally, this part uses evaluation matrixes to make standardization evaluation of single index, and ultimately reaches to comprehensive evaluation of multi-indexes weighted, which is namely respectively the calculation of every index to get the comprehensive development effectiveness. The article constructs the indexes system that is suitable for comprehensive evaluation, and the evaluation model indicates that the development benefits are good. From the inverted analysis to the evaluation results, the key factors are "comprehensive decline rate" and "natural decline rate" in technology indexes affecting development effectiveness. Therefore, strengthening the control to "comprehensive decline rate" and "natural decline rate" can better achieve the stable or increasing oilfield production. |
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